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19,918 real negative results, null findings, and replication failures · Negative / Null Result Report. Search the index →

WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record compiled from open scholarly databases; the abstract is shown in full only where the paper is openly licensed, otherwise a short excerpt under fair use. Classifications are automated and approximate.

Negative / Null Result ReportOpen accessComputer Science

AnxietyFaceTrack: A Smartphone-Based Non-Intrusive Approach for Detecting Social Anxiety Using Facial Features

Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone · 2025 · arXiv

Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video

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Negative / Null Result ReportOpen accessComputer Science

A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting

Syed Kazmi, Berk Gorgulu, Mucahit Cevik et al. · 2023 · arXiv

Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combine

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Negative / Null Result ReportMedicine

Amuc_1100 alleviates HFD-induced hepatic lipid accumulation via gut microbiota in zebrafish: insights from the role of intestinal 14-3-3β/α-A.

Ding, Lou, Liang et al. · 2026 · Journal of animal science and biotechnology

Amuc_1100, the most abundant outer membrane protein of Akkermansia muciniphila, alleviates high-fat diet (HFD)-induced hepatic lipid accumulation and modulates gut microbiota in fish; however, its mechanism and mediators remain unknown.…

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Negative / Null Result ReportOpen accessEngineering

Neuro-MoBRE: Exploring Multi-subject Multi-task Intracranial Decoding via Explicit Heterogeneity Resolving

Di Wu, Yifei Jia, Siyuan Li et al. · 2025 · arXiv

Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, existing decoding approaches largely remain constrained to single-task scenarios and individual subjects, limiting their broader applicability and generalizability. Efforts towards creating large-scale neurophysiological foundation models have shown promise, but continue to struggle with significant challenges due to pervasive data heterogeneity across subjects and decoding tasks. Simply increasing model parameters and da

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Negative / Null Result ReportOpen accessComputer Science

Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions

Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana · 2019 · arXiv

We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.

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Negative / Null Result Report

Succession if the reorganization of corporation is failed

Arsenii Ivanovich Alekseev · 2024 · Право и политика

The article analyzes the consequences of the failed reorganization of the corporation and the recognition by the court of the reorganization of the corporation as invalid by referring to the nature and content of such categories as the…

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Negative / Null Result Report

Management of Failed Spinal Anesthesia

Wiji Hastuti · 2026 · Jurnal Komplikasi Anestesi

The management of failed spinal anesthesia is a significant challenge in surgical procedures. Spinal anesthesia failure can occur either partially or totally, often leading to conversion to general anesthesia, which adds clinical risks and…

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Negative / Null Result ReportOpen accessComputer Science

Hyperparameters in Continual Learning: A Reality Check

Sungmin Cha, Kyunghyun Cho · 2024 · arXiv

Continual learning (CL) aims to train a model on a sequence of tasks (i.e., a CL scenario) while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge). The dominantly adopted conventional evaluation protocol for CL algorithms selects the best hyperparameters (e.g., learning rate, mini-batch size, regularization strengths, etc.) within a given scenario and then evaluates the algorithms using these hyperparameters in the same scenario. However, this protocol has significant shortcomings: it overestimates the CL capacity of algorithms and relies

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Negative / Null Result ReportOpen accessComputer Science

Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing

Jiarui Xie, Mutahar Safdar, Andrei Mircea et al. · 2024 · arXiv

Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been thoroughly investigated due to a lack of formal evaluation methods. Reproducibility, a critical component of trustworthy artificial intelligence, is achieved when an independent team can replicate the findings or artifacts of a study using a different experimental setup and achieve comparable performance. In many publications, critical information nec

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Negative / Null Result ReportOpen accessComputer Science

Dissecting Role Cognition in Medical LLMs via Neuronal Ablation

Xun Liang, Huayi Lai, Hanyu Wang et al. · 2025 · arXiv

Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simulations. A common practice, Prompt-Based Role Playing (PBRP), instructs models to adopt different clinical roles (e.g., medical students, residents, attending physicians) to simulate varied professional behaviors. However, the impact of such role prompts on model reasoning capabilities remains unclear. This study introduces the RP-Neuron-Activated Evaluation Framework(RPNA) to evaluate whether role prompts induce disti

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Negative / Null Result ReportOpen accessPhysics

Dynamical Constraints on Mercury's Collisional Origin

Matthew S. Clement, Nathan A. Kaib, John E. Chambers · 2019 · arXiv

Of the solar system's four terrestrial planets, the origin of Mercury is perhaps the most mysterious. Modern numerical simulations designed to model the dynamics of terrestrial planet formation systematically fail to replicate Mercury; which possesses just 5% the mass of Earth and the highest orbital eccentricity and inclination among the planets. However, Mercury's large iron-rich core and low volatile inventory stand out among the inner planets, and seem to imply a violent collisional origin. Because most algorithms used for simulating terrestrial accretion do not consider the effects of col

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Negative / Null Result ReportOpen accessComputer Science

Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction

Sarah Ball, Simeon Allmendinger, Frauke Kreuter et al. · 2025 · arXiv

Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing model-generated responses to gold-standard survey results, fundamental questions about the validity and reliability of using LLMs as substitutes for human respondents remain. Our study provides a technical analysis of how demographic attributes and prompt variations influence latent opinion mappings in large language models (LLMs) and evaluates their suitability for survey-based predictions. Using 14 different model

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Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance

Interbank network reconstruction enforcing density and reciprocity

Valentina Macchiati, Piero Mazzarisi, Diego Garlaschelli · 2024 · arXiv

Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating the number of directed cycles, which on the other hand play a crucial role in determining graph spectra and hence the degree of network stability and systemic risk. Here we address this challenge by exploiting the hypothesis that the stati

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Negative / Null Result ReportOpen accessComputer Science

RILe: Reinforced Imitation Learning

Mert Albaba, Sammy Christen, Thomas Langarek et al. · 2024 · arXiv

Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings poses a significant challenge due to the vast search space. Traditional reinforcement learning (RL) requires extensive manual effort for reward function engineering. Inverse reinforcement learning (IRL) uncovers reward functions from expert demonstrations but relies on an iterative process that is often computationally expensive. Imitation learning (IL) provides a more efficient alternative by directly comparing an agent's actions to expert demonstrations; how

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Negative / Null Result ReportOpen accessPhysics

Understanding Balmer Decrements in T Tauri stars in terms of Multiflow Magnetospheric Accretion

Naiara Patiño, Nuria Calvet, Gladis Magris et al. · 2026 · arXiv

Magnetospheric accretion is the paradigm for accretion in Classical T-Tauri Stars (CTTS). However, the standard, one-flow magnetospheric accretion model fails to replicate important characteristics such as the observed Balmer decrements. We address this limitation by adopting a model with two axisymmetric magnetospheric accretion flows of different accretion rates and geometries. We calculate the fluxes of the hydrogen $H_α$, $H_β$, and $H_γ$ lines of each flow with the magnetospheric accretion model and use Bayesian statistics to fit the Balmer line fluxes of 139 CTTS in the Orion OB1b subass

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Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance

Deep Smoothing of the Implied Volatility Surface

Damien Ackerer, Natasa Tagasovska, Thibault Vatter · 2019 · arXiv

We present a neural network (NN) approach to fit and predict implied volatility surfaces (IVSs). Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to value other financial instruments. In other words, low training losses are as important as generalization capabilities. Importantly, IVS models need to generate realistic arbitrage-free option prices, meaning that no portfolio can lead to risk-free profits. We propose an approach guaranteeing the absence of arbitrage opportunities by penalizing the loss using soft const

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Negative / Null Result ReportOpen accessMathematics

How to Tell When a Result Will Replicate: Significance and Replication in Distributional Null Hypothesis Tests

Fintan Costello, Paul Watts · 2022 · arXiv

There is a well-known problem in Null Hypothesis Significance Testing: many statistically significant results fail to replicate in subsequent experiments. We show that this problem arises because standard `point-form null' significance tests consider only within-experiment but ignore between-experiment variation, and so systematically underestimate the degree of random variation in results. We give an extension to standard significance testing that addresses this problem by analysing both within- and between-experiment variation. This `distributional null' approach does not underestimate exper

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Negative / Null Result ReportOpen accessComputer Science

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks

Xin Ding, Yongwei Wang, Zuheng Xu · 2023 · arXiv

Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can produce subpar fake images due to limited training data. Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling. We present a novel NDA approach called Dua

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Negative / Null Result ReportOpen accessComputer Science

A Replicability Study of XTR

Rohan Jha, Reno Kriz, Benjamin Van Durme · 2026 · arXiv

The XTR (conteXtual Token Retrieval) algorithm is a modification to ColBERT retrieval that avoids the costly step of fully gathering and reranking the candidates' embeddings by imputing their missing similarity scores from the initial token retrieval step. The original work proposes a modified training objective as necessary for effective XTR retrieval, arguing that standard ColBERT token scoring is unsuitable for imputation. In this paper, we replicate both the XTR retrieval algorithm and its modified training objective, and extend the evaluation to knowledge-distillation (KD) training and ef

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Negative / Null Result ReportOpen accessPhysics

Breakdown of Boltzmann-type Models for the Alignment of Self-propelled Rods

Patrick Murphy, Misha Perepelitsa, Ilya Timofeyev et al. · 2023 · arXiv

Studies in the collective motility of organisms use a range of analytical approaches to formulate continuous kinetic models of collective dynamics from rules or equations describing agent interactions. However, the derivation of these kinetic models often relies on Boltzmann's hypothesis of "molecular chaos", that correlations between individuals are short-lived. While this assumption is often the simplest way to derive tractable models, it is often not valid in practice due to the high levels of cooperation and self-organization present in biological systems. In this work, we illustrated this

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